mljsml-ransac
Fit a model to noisy data by excluding outliers. This is an implementation of the RANSAC algorithm..
Installation
$ npm i ml-ransac
Examples
Finding the best linear regression which doesn't take the outliers into account:
import { levenbergMarquardt } from 'ml-levenberg-marquardt'; // library allowing you to find a regression of any type
import { ransac } from 'ml-ransac';
// defining the model function: a line
export type LineFunction = (x: number) => number;
export function line([a, b]: number[]): LineFunction {
return (x: number) => a * x + b;
}
// defining the fit function: we create a linear regression using levenberg-maquard
export interface LinearRegressionOptions {
/**
* Initial parameter values.
*
* @default [0,0]
*/
initialValues?: number[];
}
export function linearRegression(
source: number[],
destination: number[],
options: LinearRegressionOptions = {},
): number[] {
const { initialValues = [0, 0] } = options;
const result = levenbergMarquardt({ x: source, y: destination }, line, {
initialValues,
});
return result.parameterValues;
}
// data
const source = [-2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10];
const destination = [-6, -2, 0, 5, 0, 0, -1, 1, 0, 0, -1, 0, 3];
// finding the best model
const result = ransac(source, destination, {
distanceFunction,
fitFunction,
modelFunction,
threshold: 1.1,
sampleSize: 2,
maxNbIterations: 10,
seed: 0,
});
// result is :
// {
// nbIterations: 10,
// modelParameters: [0, 0],
// inliers: [2, 4, 5, 6, 7, 8, 9, 10, 11],
// error: 1,
// }